Secure Public API MCP Server

Secure Public API MCP Server

OAuth-protected FastMCP server exposing eight read-only public APIs (currency, weather, art, pets, dictionary, random images, vehicles) via MCP tools, with optional Gemini LLM-based routing for query selection.

Category
Visit Server

README

Secure Public API MCP Server

FastAPI hosts an OAuth-protected FastMCP server for eight read-only public APIs. The /query convenience endpoint accepts request_id and request_message, then selects the relevant allow-listed API tools.

Routing modes

By default, routing is deterministic and does not need an LLM or API key:

export LLM_ROUTING_ENABLED=false

To enable Gemini reasoning, configure Google Cloud Application Default Credentials (ADC) and restart the server:

export LLM_ROUTING_ENABLED=true
export GOOGLE_CLOUD_PROJECT='your-gcp-project-id'
export GOOGLE_CLOUD_LOCATION='us-central1' # optional
# Optional; defaults to gemini-2.5-flash
export GEMINI_MODEL='gemini-2.5-flash'

For local development, authenticate the Google SDK once:

gcloud auth application-default login

For a deployed workload, attach a service account with Vertex AI access and use its ambient credentials (or set GOOGLE_APPLICATION_CREDENTIALS to the service-account credential file). Do not put a Gemini API key in this project.

With LLM routing enabled, Gemini receives a constrained prompt and must return validated JSON naming only the eight local tools. It cannot access arbitrary URLs, execute code, or bypass authorization. The service validates and bounds each selected argument before calling any public API. If the flag is enabled without project/ADC configuration or the SDK, /query returns 503; it does not silently switch to rule routing.

Local setup

cd /Users/nkrishnakumar/Documents/Mohaan/public-api-mcp
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Create the local configuration before starting the app. It prompts for a local password, generates the JWT and OAuth client secrets, and writes every required export to .env.local (which is Git-ignored):

python3 setup.py
source .env.local
uvicorn main:app --reload

Enable Gemini/Vertex AI routing while creating the configuration:

python3 setup.py --llm --gcp-project 'your-gcp-project-id'
source .env.local
gcloud auth application-default login
uvicorn main:app --reload

setup.py configures APP_ENV, JWT issuer/audience/expiry/signing key, allow-listed users and OAuth clients, LLM mode, Google Cloud project/location, and Gemini model. It also exports LOCAL_OAUTH_CLIENT_ID and LOCAL_OAUTH_CLIENT_SECRET for local curl testing. Re-run it whenever you want new local secrets; it replaces .env.local.

The service runs at http://127.0.0.1:8000; OpenAPI documentation is at /docs.

Authentication and authorization

POST /token requires both an approved OAuth client (client_id and client_secret) and an approved user credential. It issues a short-lived JWT containing issuer, audience, expiry, role, and scopes. Every /mcp/* request and /query needs this Bearer token. user has read; admin has read and write. All current public API tools are read-only.

curl -X POST http://127.0.0.1:8000/token \
  -H 'Content-Type: application/x-www-form-urlencoded' \
  -d "username=local-user&password=<the-password-you-entered>&client_id=$LOCAL_OAUTH_CLIENT_ID&client_secret=$LOCAL_OAUTH_CLIENT_SECRET"
curl -X POST http://127.0.0.1:8000/query \
  -H 'Authorization: Bearer <access_token>' \
  -H 'Content-Type: application/json' \
  -d '{"request_id":"local-001","request_message":"Show Texas weather alerts and the EUR exchange rate"}' | jq

MCP clients connect to GET /mcp/sse with the same Bearer token and call the tools get_currency_rate, get_artworks, get_dog_pic, get_cat_pic, get_dictionary_meaning, get_weather_alerts, get_random_pic, and get_vehicle_manufacturers.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured